Software Development Lifecycle Vocabulary
Normative vocabulary and taxonomy for software development lifecycle tools, platforms, and practices, covering the full spectrum from planning through operations with modern DevOps and platform engineering terminology.
21 Terms
4 Groups
6 With Aliases
Updated 2026-05-02
Software Development Lifecycle Vocabulary is a controlled vocabulary published by Software Development Lifecycle on the APIs.io network, defining 21 terms.
The terms are organized into 4 groups — phases, practices, tools-and-platforms, and metrics.
Defined terms include Planning, Development, Build, Testing, and Security Scanning.
6 terms carry alternate labels, so a consumer can resolve the synonyms Software Development Lifecycle accepts to the canonical term.
A controlled vocabulary pins the words an API’s schemas, parameters and documentation use to one agreed definition each — the shared language a human or an agent needs to read the API the same way its authors do.
Planning
The initial lifecycle phase where requirements are gathered, work is decomposed into tasks and stories, capacity is estimated, and delivery roadmaps are established.
Development
The implementation phase where developers write, review, and integrate code into shared repositories, following branching strategies and code review practices.
Build
The automated compilation, packaging, and artifact creation phase that transforms source code into deployable software units.
Testing
The quality verification phase involving automated unit, integration, end-to-end, performance, and security tests that must pass before code advances through the pipeline.
Security Scanning
Automated security analysis embedded in the development lifecycle, including SAST, DAST, SCA, and container scanning to detect vulnerabilities before code reaches production.
Deployment
The process of moving validated software artifacts through staging and production environments using automated pipelines with rollback capability.
Operations
The ongoing management and monitoring of deployed software including incident response, performance tuning, and maintenance activities.
Continuous Integration
The practice of frequently merging developer code changes into a shared repository and automatically running build and test pipelines to detect integration issues early.
CI
Continuous Delivery
The practice of automating the release process so that software can be deployed to production reliably at any time with minimal manual intervention.
CD
Infrastructure as Code
The practice of managing and provisioning infrastructure through machine-readable configuration files rather than manual processes, enabling version-controlled and reproducible environments.
IaC
GitOps
An operational framework using Git as the single source of truth for declarative infrastructure and application configuration, with automated reconciliation between desired and actual state.
Shift Left
The practice of moving security and quality testing earlier in the development lifecycle to catch and fix issues when they are least expensive to remediate.
Trunk-Based Development
A source control branching model where developers merge small, frequent updates to a shared "trunk" or main branch, reducing merge conflicts and integration complexity.
Version Control System
Software that tracks changes to files over time, allowing teams to recall specific versions later and collaborate on code without conflicts.
VCSSource Control
Issue Tracker
A system for creating, assigning, and tracking software defects, feature requests, and work items throughout the development lifecycle.
Artifact Registry
A repository for storing and managing build artifacts including container images, packages, and binaries produced by CI/CD pipelines.
Internal Developer Portal
A centralized web application that provides developers with self-service access to tools, documentation, service catalogs, and infrastructure templates needed throughout the development lifecycle.
IDP
Deployment Frequency
A DORA metric measuring how often an organization successfully releases software to production, indicating delivery agility.
Lead Time for Changes
A DORA metric measuring the time from code commit to code running in production, reflecting the efficiency of the delivery pipeline.
Change Failure Rate
A DORA metric measuring the percentage of deployments that cause a failure in production requiring a rollback or hotfix.
Mean Time to Recovery
A DORA metric measuring how quickly a team can restore service after a production incident or failed deployment.
MTTR